Fit-to-Targets for the Desired Sensation Level Version 5.0a Hearing Aid Prescription Method for Children
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to measure the range of fit to Desired Sensation Level version 5.0 (DSL v5.0) targets in pediatric practice environments. Results will be used in the future to develop clinical-aided speech intelligibility index typical performance data. METHOD: Clinical partners collected data from 161 final hearing aid settings for children aged ≤ 10 years. Measured data were obtained by performing 2-cm3 coupler-simulated real-ear measurements using the DSL v5.0 implementation on the Audioscan VF-1 (Etymonic Design Inc., Dorchester, ON, Canada) for soft, average, and loud speech inputs and maximum hearing aid output levels. RESULTS: Fittings were within ± 5-dB root-mean-square (RMS) error of target for 77%, 80%, and 82% of fittings for the soft, medium, and loud speech test levels, respectively. Aided maximum power output measures were within ± 5-dB RMS error in 72% of cases. Degree of hearing loss, test frequency, and frequency by test level were significant factors in deviation from target. The range of aided speech intelligibility index values exhibited a strong correlation with the hearing levels of the children tested. CONCLUSION: This study provides evidence that typical hearing aid fittings for children can be achieved within ± 5-dB RMS error of the DSL v5.0 target. Greater target deviations were observed at extreme frequencies and as the severity of hearing loss increased.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".